Methods and systems for offering financial products

ABSTRACT

The disclosure provides systems and methods for analysis of financial data and customization of financial products. Time-dependent data is obtained for a relatively large set of users, and from the data are determined a relatively small set of trajectories that model user behaviour. New users are characterized according to the trajectories and financial products are automatically personalised to suit the new user.

FIELD OF THE INVENTION

In embodiments, the technical field of the invention is methods andsystems for analysis of financial data and customization of financialproducts.

BACKGROUND

Financial institutions are continuously making efforts to improve theproducts that they offer to users. Such improvements typically take oneof two forms—improving the user experience (i.e., the quality,relevancy, ease of use, etc.), and improving the return for thefinancial institution (i.e., increasing profit, reducing risk,increasing uptake by the client, etc.). In order to make suchimprovements, financial institutions rely on a variety of informationsuch as conventional statistics (e.g., daily or monthly means, maximums,and/or minimums of user accounts), job status, current debt ordebt-to-income ratio, or the like. These conventional statistics areoften, however, relatively mediocre predictors of user behavior, loanperformance, and risk, and improved methods of predicting such factorsare continuously sought by financial institutions.

Financial products are increasingly offered to users via mobileplatforms, yet the mobile platform has remained largely as a conduitrather than a source of primary data for the financial institutions.Nevertheless, mobile penetration in many rural and developingareas—i.e., regions where traditional banking services are relativelyless common—is quite high, and often those mobile services includemobile money accounts and mobile banking services. Financialinstitutions would be well served using non-traditional sources of datafor offering tailor-made financial services and products.

SUMMARY OF THE INVENTION

In an aspect, then, is a method for processing financial informationcomprising: applying a group-based trajectory model (GBTM) algorithm totime-dependent financial data for a first financial variable for aplurality of users to determine a plurality of first financial variabletrajectories; for a set of first financial variable time-dependent datafor a new user, determining a best fitting trajectory from the pluralityof first financial variable trajectories; transmitting a message, via adistributed network, to a recipient device selected from a user deviceassociated with the new user and a provider device associated with aprovider, the message comprising content determined at least in part bythe best fitting trajectory, the message configured to alter a userinterface of the recipient device to display the content, wherein thecontent comprises an offer for a financial product, and wherein at leastone variable pertaining to the financial product is determined at leastin part by the best fitting trajectory; and coordinating reconfigurationof the recipient device based on the offer for a financial product. Inembodiments:

the recipient device is a user device;

the recipient device is a user device selected from a mobile phone and atablet;

the recipient device is a provider device;

the recipient device is a provider device selected from a server, amobile device (e.g., mobile phone, tablet, etc.), and desktop computer;

the reconfiguration is a non-volatile change to the recipient device;

the reconfiguration of the recipient device involves any combination ofthe following: changing a setting in the device; altering a userinterface of the recipient device to display one or more variables aboutthe offered financial product (e.g., status of the offer, status of anaccepted product, outstanding balance, payments made, credit available,and the like); altering an application stored in a memory of therecipient device to display a new field, the new field related to theoffered financial product (e.g., outstanding balance, payments made,credit available, and the like); set an alert schedule to providescheduled reminders (e.g., visual or audible alerts, email reminders,SMS reminders, etc.) to the user or provider; and updating a profile ofthe user on the provider device.

the method further comprises receiving via the distributed network theset of first financial variable time-dependent data for the new user;

the first financial variable is selected from a savings account balance,an outstanding credit balance, a credit capacity, a length of credithistory, a number of deposits, a number of withdrawals a number ofloans, a number of repayments, a number of balance queries an amount ofinterest earned over time, an amount of largest deposit, an amount oflargest withdrawal, an average deposit, and an average withdrawal;

the plurality of first financial variable trajectories comprises between2 and 10 trajectories;

further comprising applying the GBTM algorithm to time-dependentfinancial data for a plurality of additional financial variable for theplurality of users to determine a plurality of financial variabletrajectories for each of the plurality of additional financialvariables;

further comprising applying the GBTM algorithm to time-dependentfinancial data for a plurality of additional financial variable for theplurality of users to determine a plurality of financial variabletrajectories for each of the plurality of additional financialvariables, and further comprising determining best fitting trajectoriesfor each of the additional financial variables for the new user from theplurality of financial variable trajectories for each of the pluralityof additional financial variables;

the message is transmitted to the provider device and wherein thecontent further comprises characterization of the new user based on thebest fitting trajectory;

the at least one variable is selected from a loan amount, an interestrate, and a repayment schedule;

further comprising applying the GBTM algorithm to time-dependentfinancial data for a plurality of additional financial variable for theplurality of users to determine a plurality of financial variabletrajectories for each of the plurality of additional financialvariables, and the message is transmitted to the provider device andwherein the content further comprises characterization of the new userbased on the best fitting trajectory;

further comprising applying the GBTM algorithm to time-dependentfinancial data for a plurality of additional financial variable for theplurality of users to determine a plurality of financial variabletrajectories for each of the plurality of additional financialvariables, and the at least one variable is selected from a loan amount,an interest rate, and a repayment schedule; and

the first financial variable is selected from a savings account balance,an outstanding credit balance, a credit capacity, a length of credithistory, a number of deposits, a number of withdrawals a number ofloans, a number of repayments, a number of balance queries an amount ofinterest earned over time, an amount of largest deposit, an amount oflargest withdrawal, an average deposit, and an average withdrawal, andthe message is transmitted to the provider device and wherein thecontent further comprises characterization of the new user based on thebest fitting trajectory.

In an aspect is a method comprising: obtaining time-dependent financialdata for a plurality of users between a first time and a second time;applying an algorithm to reduce the time-dependent financial data for aplurality of users to a plurality of representative trajectories;obtaining time-dependent financial data for a new user between the firsttime and the second time; comparing time-dependent financial data forthe new user with the trajectories, and identifying the trajectory ofbest fit; determining a financial product for the new user at least inpart based on the trajectory of best fit; automatically executing anelectronic fund transfer into a user account associated with the newuser based on the determined financial product; and altering a userinterface to display the automatic transfer of funds. In embodiments:

the financial product is a loan, and where the loan amount, interestrate, and repayment schedule are determined based on the trajectory ofbest fit;

the plurality of users is greater than 1000 users, and wherein obtainingtime-dependent financial data for the plurality of users between a firsttime and a second time comprises obtaining daily data for a period of atleast one week;

the number of trajectories is at least 10 times fewer than the number ofusers;

the user account is a mobile money account associated with a mobileaccount;

the financial product is further determined based on telecom datapertaining to the new user;

the time-dependent financial data for a plurality of users comprisesdata for a plurality of financial variables, and wherein the algorithmis applied separately to each of the plurality of financial variables;

the user interface is on a user device associated with the new user, andwherein the method further comprises transmitting a message to the userdevice, the message configured to alter the user interface to indicatethe determined financial product and an electronic funds transferstatus;

the financial product is a loan, and where the loan amount, interestrate, and repayment schedule are determined based on the trajectory ofbest fit, and the number of trajectories is at least 10 times fewer thanthe number of users;

the user account is a mobile money account associated with a mobileaccount, and the financial product is further determined based ontelecom data pertaining to the new user;

the user interface is on a user device associated with the new user, andwherein the method further comprises transmitting a message to the userdevice, the message configured to alter the user interface to indicatethe determined financial product and an electronic funds transferstatus, and the number of trajectories is at least 10 times fewer thanthe number of users;

the user interface is on a user device associated with the new user, andwherein the method further comprises transmitting a message to the userdevice, the message configured to alter the user interface to indicatethe determined financial product and an electronic funds transferstatus, and the financial product is a loan, and where the loan amount,interest rate, and repayment schedule are determined based on thetrajectory of best fit; and

the user interface is on a user device associated with the new user, andwherein the method further comprises transmitting a message to the userdevice, the message configured to alter the user interface to indicatethe determined financial product and an electronic funds transferstatus, and the user account is a mobile money account associated with amobile account, and the financial product is further determined based ontelecom data pertaining to the new user.

In aspects is a system comprising: a processor; a memory coupled to theprocessor, the memory configured to store program instructions forinstructing the processor to carry out the methods as above. Inembodiments, the system further comprises a trajectory module configuredto apply the GBTM algorithm to determine the plurality of firstfinancial variable trajectories. In embodiments, the system furthercomprises a trajectory comparison module configured to compare the setof first financial variable time-dependent data for the new user todetermine the best fitting trajectory from the plurality of firstfinancial variable trajectories.

These and other aspects of the invention will be apparent to one ofskill in the art from the description provided herein, including theexamples and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a flow chart showing data processing by a systemaccording to an aspect of the invention.

FIG. 2 provides a flow chart showing data input and output according toan aspect of the invention.

FIG. 3 provides a set of steps for carrying out processing of historicaldata according to an aspect of the invention.

FIG. 4 provides a set of steps for carrying out processing of user dataaccording to an aspect of the invention.

FIG. 5 provides a set of steps for processing a message according to anaspect of the invention.

FIG. 6 illustrates a computer system in accordance with which one ormore components/steps of the techniques of the invention may beimplemented according to an aspect of the invention.

FIG. 7 depicts a cloud computing environment according to an aspect ofthe invention.

FIG. 8 depicts abstraction model layers according to an aspect of theinvention.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

In an aspect is a method for processing financial informationcomprising: applying a group-based trajectory model (GBTM) algorithm totime-dependent financial data for a first financial variable for aplurality of users to determine a plurality of first financial variabletrajectories; for a set of first financial variable time-dependent datafor a new user, determining a best fitting trajectory from the pluralityof first financial variable trajectories; transmitting a message, via adistributed network, to a user device associated with the new user or toa provider device associated with a provider, the message comprisingcontent determined at least in part by the best fitting trajectory, themessage configured to alter a user interface of the user device orprovider device to display the content, wherein the content comprises anoffer for a financial product, and wherein at least one variablepertaining to the financial product is determined at least in part bythe best fitting trajectory.

In an aspect is a method comprising: obtaining time-dependent financialdata for a plurality of users between a first time and a second time;applying an algorithm to reduce the time-dependent financial data for aplurality of users to a plurality of representative trajectories;obtaining time-dependent financial data for a new user between the firsttime and the second time; comparing time-dependent financial data forthe new user with the trajectories, and identifying the trajectory ofbest fit; determining a financial product for the new user at least inpart based on the trajectory of best fit; automatically executing anelectronic fund transfer into a user account associated with the newuser based on the determined financial product; and altering a userinterface to display the automatic transfer of funds.

In aspects are system(s) configured to carry out the methods describedherein. The system comprises a processor and a memory coupled to theprocessor, the memory configured to store program instructions forinstructing the processor to carry out the method. The systems mayfurther comprise additional components, including multiple processors,multiple independent memories, and the like, and that any suitablesystem architecture (e.g., localized, cloud-based, etc.) may be used.Further details are provided herein. It will be appreciated, however,that certain components of such systems, and further certain steps ofthe associated methods, may be omitted from this disclosure for the sakeof brevity. The omitted components and steps, however, are merely thosethat are routinely used in the art and would be easily determined andimplemented by those of ordinary skill in the art using nothing morethan routine experimentation. Throughout this specification, wherehardware is described, it will be assumed that the devices and methodsemploying such hardware are suitably equipped with necessary software(including any firmware) to ensure that the devices/methods are fit forthe described purpose. It is the case that implementation of the methodsand systems herein, including development of any necessary andaccompanying components (whether described herein or omitted), will beachievable by one skilled in the art based on this disclosure in thecontext of commonly available information.

Throughout this disclosure the term “panel data” is used to refer todata collected about a specific variable (e.g., savings account balancesor the like) over a period of time. That period of time may range from afew days to several years or more, and specifically may be in the rangeof 5-1500 days, or 1-12 months, or 1-6 months, or 3-6 months, or anotherappropriate range. Panel data may be obtained from more than onevariable and the timeframe for data collection may be similar orindependently selected for each variable.

Throughout this disclosure, there are discussed “providers” and “users.”A provider is an entity such as a corporate, government, or other entitythat offers products to user. Particularly, the provider is a financialservices provider such as a banking entity or the like, and a user is acustomer of the provider. Throughout this disclosure, a “new user” ismeant a user that may be offered a product by the provider based on oneor more analysis or analyses carried out as described herein. A “newuser” may be a user that has not previously been offered a product ormay be a user that has been previously offered products and is now inconsideration for a new product offer.

The GBTM, in embodiments, takes panel data and creates a preselectednumber of trajectories that model the data. For example, from panel datafor “y” different users having “y” individual bank accounts (i.e., userseach having a savings account), and the savings account balancesmeasured periodically (e.g., daily, or every other day, etc.) over aspecific period of time (e.g., 1, 2, 4, 8, or 12 weeks, or 1, 2, 3, 6,or 12 months, or 1, 2, or 3 years, or more than 3 years), the algorithmgenerates a number “k” of model trajectories. In embodiments, the numberof users providing the test panel data is at least 500, 1000, 2000,3000, 5000, or 10000 users. In embodiments, the number of trajectories“k” is determined by the provider and may be 5, 10, or 15, or any otherspecifically desired number, or in the range of 2-20, or 2-10, or 3-10,or 4-15, or 5-15, or the like. In embodiments, the number oftrajectories is at least 10 times fewer (or 20, 30, 40, or 50 timesfewer) than the number of users from which the test panel data isderived. The accuracy of the model is dependent on the value selectedfor k, and various algorithms (e.g., meta analysis) can be used todetermine the optimal value of k for a given set of data. Furthermore,the panel data can be updated with new data and the k trajectoriesrecalculated with the GBTM algorithm. This updating can occur for everyaddition of data to the panel data, or at predetermined intervals.

Throughout this disclosure, two types of data are described—“test paneldata” (which may, in instances herein, be simply referred to as “paneldata”) and “new user panel data”. The test panel data is data that isused by a provider (e.g., a banking entity) to generate the ktrajectories by the GBTM algorithm, whereas the new user panel data isdata obtained for a new user and that is fit to one or more of the ktrajectories calculated from the test panel data, for example in orderto predict one or more characteristics about the new user or todetermine a suitable financial product for the new user.

In embodiments, the test panel data may be savings account balances fora plurality of users each having a savings account. The GBTM model isused on test panel data in order to identify k trajectories that arelikely to be applicable to a new user. Accordingly, in embodiments, thepanel data is selected from a demographic or set of test users thatshare characteristics with a likely new user. For example, the testpanel data may be identified from users having a specific level ofreported income, or a specific location for a home address, or the like.The users can be actual users of a provider (i.e., a banking entity) andthe data can be actual account balances from the provider's records.Alternatively, the panel data can be aggregated from a plurality ofproviders. If necessary, the data can be suitably anonymized. Inaddition or in the alternative to savings account balances, othervariables can be used. For example, data can be the frequency a userlogs into an account, or the frequency of changes (deposits and/orwithdrawals) in an account, or the like, provided that the data showsactivity (or a lack of activity) over time.

All data used by the system can be housed in a single location, or canbe stored in a non-localized (e.g., cloud) fashion. Herein, a collectionof panel data and, optionally, other types of data may be stored in adata warehouse. The data warehouse may be maintained and operated by theprovider, in embodiments. In embodiments, telecom data is not storedwithin the data warehouse but is accessible to the provider (i.e., to aserver operated by the provider) such as via a distributed network.

In addition to the test panel data, other data may be used to improvethe accuracy of the test panel data. Examples of such data includetelecom data (e.g., for telecom users having a prepaid account, thebalance of the account, or call records), mobile money data (e.g.,balances in a mobile wallet or other mobile money account), or the like.In other embodiments and where appropriate, static data (i.e., data thatis not recorded at intervals over time) may be used in order to improveaccuracy.

For a new user, such as a new user desiring a financial product, newuser panel data can be obtained. In embodiments, such data may befinancial time-dependent data, such as savings account balances overtime, or the like. In embodiments, “x” types of data (where “x” may be1, 2, 3, 4, 5, or more than 5) are gathered pertaining to the new user(e.g., a plurality of financial time-dependent data types), and thetime-dependent data for each type of data is compared to the ktrajectories that are determined using the GBTM applied to test paneldata for that type of data. Using the single example of savings accountbalance as representative, the system obtains savings account balancesover a period of time from the new user's savings account. The resultingnew user panel data is compared to the k trajectories that aredetermined by the system for test panel data. The trajectory of best fitto the new user panel data is selected and used by the system asdescribed herein. In addition or in the alternative to savings accountbalance, other types of data can be used: an outstanding credit balance,a credit capacity, a length of credit history, a number of deposits, anumber of withdrawals a number of loans, a number of repayments, anumber of balance queries an amount of interest earned over time, anamount of largest deposit, an amount of largest withdrawal, an averagedeposit, or an average withdrawal, or combinations thereof.

Panel data can be obtained in any convenient way and from any suitablesource. For example, batch data can be obtained from pre-existing datastores and used as panel data with modifications if necessary.Modifications include altering the formatting of the data, augmentingthe data with additional data from a separate database or collectedspecifically for the task, anonymizing the data, or the like. Also forexample, panel data can be created for a provider by aggregating datafrom a variety of sources by a third party or by the provider itself.Also for example, panel data can be created afresh by a provider, e.g.,by initiating data collection over a predetermined period of time andfor a selected group of users. All of the panel data can be formattedand stored in any convenient data structure such as a database or thelike. Receipt and processing of panel data according to the processesherein can be fully or partially automated.

New user panel data can be obtained in any convenient way and from anysuitable source. For example, the new user panel data can be obtainedfrom the user him/herself, e.g., in the form of a questionnaire or thelike. New user panel data can be obtained from a previous provider towhich the user has/had a relationship, e.g., a prior lending or bankinginstitution. New user panel data can be obtained from a credit trackinginstitution or some other third party (e.g., telecom providers, etc.)with access to data of this nature. New user panel data can beobtained/generated directly by the provider as new data, e.g., bycreating a “trial period” during which the new user is monitored for theexpress purpose of utilizing the systems and methods herein. New userpanel data can be formatted specifically in order to be compatible withthe systems and methods herein, and may be stored in any convenient datastructure such as a database or the like. Receipt and processing of newuser panel data can be fully or partially automated. For example, a newuser can indicate a previous banking institution and the system thenautomatically queries, receives, and processes (according to the methodsherein) the data from the banking institution.

In order to determine the trajectory of best fit for new user paneldata, the systems herein comprise a trajectory comparison module. Inembodiments, for example, the trajectory comparison module is configuredto compare the set of first financial variable time-dependent (panel)data for the new user to determine the best fitting trajectory from theplurality of first financial variable trajectories determined from testpanel data.

In embodiments, the new user panel data is compared to trajectoriesdetermined by the system using the GBTM algorithm, and the new user ischaracterized according to such comparison. Based on thecharacterization, the provider automatically determines at least onevariable pertaining to a product that can be offered to the new user.The variable may be further modified based on other data from the datawarehouse, including comparison of other types of data pertaining to thenew user with trajectories determined from test panel data. A pluralityof variables may be determined in this manner, if desired. The systemmay further automatically determine a specific product or a class ofproducts that can be offered to a new user based on the analysis of newuser panel data. In embodiments, financial product is a loan and the atleast one variable is selected from a loan amount, an interest rate, anda repayment schedule.

In embodiments, the output of group based trajectory models provides twofundamental results for the systems and methods herein. First, for eachgroup a trajectory is provided. This trajectory is a smooth, low-orderpolynomial that attempts to capture the savings or transaction behaviour(for example) of the customers in that group over time. Simple examplesinclude generally increasing, generally decreasing, peaking and thenfalling off, etc. Second, each customer is given a probability of beingincluded in (or belonging to) one of the k trajectories. This groupmembership is similar to customer segmentation but arrived at throughthe trajectory analysis of longitudinal data. These two outputs fromGBTM models are then used as input into the systems and methods forcreating more accurate credit scores on high-frequency micro loans.

Once the system determines the at least one variable, the systemgenerates a message. The message is configured to cause one or moreactions as described in more detail herein. In embodiments, the messageis transmitted to a provider device or an output component of theprovider system. In embodiments, the message is transmitted to a userdevice, the user device comprising a user interface. In embodiments themessage may be transmitted to a third party (i.e., not the user and notthe provider). Combinations of recipients are also allowed. Inembodiments, the message is automatically generated and has a formatthat is compatible with the recipient device/system. In embodiments, thecontent of the message may comprise a characterization of the new userbased on the determined best fitting trajectory, and may furthercomprise other information (e.g., identity information about the userand/or the provider, historical information about the user and/orprovider, information about the product on offer, information about themethod used to determine the product, etc.).

In embodiments, the message is configured to cause one or more actions.Such actions include: altering the user interface of a user device or aprovider device to indicate (e.g., display, vocalize, print, orotherwise output) the determined financial product and/or the determinedat least one variable; initiate an automatic funds transfer to a useraccount from an account of the provider; indicate an electronic fundstransfer status; transfer money to a user account; and the like. Inembodiments, the user account is a bank account or, alternatively, amobile money account associated with a mobile account.

The systems and methods herein are intended to include a user device.The user device may be a mobile device such as a mobile phone, tablet,or the like, or a desktop computer in some cases. The user devicecontains a communications module. The communication module is configuredto interface with the distributed network with which the server and anyother components of the system is/are also in communication. In this waythe user device is in communication with the server and any othercomponents of the system herein, and the user device can receive outputfrom the server. Such reception can be in real time—e.g., theconnections with the network are continuous and the user device receivesmessages without any significant time delay. Such reception canalternatively be delayed—e.g., where the user device is temporarily outof communication with the network, the output is received at a time whenthe user device re-establishes such communication. The systems hereinalso involve a provider device (e.g., a server, desktop computer, nodewithin a system of networked nodes, etc.). Herein, the user device andprovider device may be referred to collectively or individually as arecipient device, particularly where one or both of these devices arerecipients of a message as described herein.

The user device contains a user interface such as a graphical userinterface (GUI) or, in some cases where necessary, an audio-only userinterface or some other type of user interface as known in the field.The user interface is configured to be modified by the output of theserver (e.g., by the message sent by the server). The modification canbe temporary (e.g., for a single viewing such as by delivery of a textmessage to the user) or can be long-lasting (e.g., present every timethe user accesses the GUI, such as modifying the user's account toinclude a new product, account, or the like, and modifying the user'sGUI accordingly to show that modification to their account). Themodification can be interactive, such as where the modification is theresult of a message containing instructions for a question/answersession with the user. This is particularly suitable where the outputseeks further input from the user, such as an acceptance of a newproduct. For an interactive message from the server, the methods canfurther include establishing a connection with the user device andtransferring data between the server and the user device (e.g., via aUSSD connection or the like).

An additional or alternative output of the systems and methods herein isautomatic initiation of any one or more of the following: an electronicprocess for delivering a product to the user; an electronic process fordelivering a service to the user; an advertisement for a product orservice configured for delivery and display on the user device;modification of a product or service for the user (either backendmodification, user-level modification, or a combination thereof);modification of a product or service generally for all users;modification of the user's account information or status; or the like.For example, the output can automatically initiate a change to theuser's account with the provider, such as changing the credit score ofthe user, or changing the credit limit of the user, or the like.

An additional or alternative output of the systems and methods herein isa predictive model or improvements in predictive models. An example ofthis method is to put temporal data from a customer into the system andget a more accurate credit score for the individual out. Then, this canbe done on a larger sample of customers, with temporal data forthousands, tens of thousands, or hundreds of thousands of customers. Insuch a way a more accurate model would emerge. Therefore, inembodiments, the model that comes out of this process is also a usefuloutput.

The systems and methods herein further involve coordinatingreconfiguration of the recipient device based on the offer for afinancial product. The reconfiguration of the recipient device involvesany combination of the following: changing a setting in the device;altering a user interface of the recipient device to display one or morevariables about the offered financial product (e.g., status of theoffer, status of an accepted product, outstanding balance, paymentsmade, credit available, and the like); altering an application stored ina memory of the recipient device (e.g., to display a new field, the newfield related to the offered financial product, such as a new field foran outstanding balance, payments made, credit available, and the like);set an alert schedule to provide scheduled reminders (e.g., visual oraudible alerts, email reminders, SMS reminders, etc.) to the user orprovider; and updating a profile of the user on the provider device. Inembodiments the reconfiguration is a non-volatile change to the device,such as a change to a setting that will affect the device until suchtime as the setting is changed again.

Data and information may be communicated between a user device and aserver. The server may be, for example, located with the provider—suchas with a bank or other financial entity. Alternatively or in addition,a remote server not located at the provider, or even a cloud computingarchitecture, or any combination of such embodiments, may be used asappropriate. Communications may be carried out via a distributednetwork. Any such suitable network now known or later developed may beused. Examples of such networks include GSM, 3G, 4G, EDGE, WiFi,Bluetooth, mesh, or other networks may be suitable. The distributednetwork can be used, for example, to receive new user panel data by theserver, to receive test panel data by the server (e.g., from a varietyof banking entities and/or user devices), to transmit messages from theserver to a user device, or the like.

Financial transaction data (payments, transfers, savings, balancequeries, etc.) across many users over time create very rich panel(longitudinal) data sets. For example, panel data provides insights intocyclical financial behavior that is critical when observing usersbetween regularly scheduled in-flows and out-flows. Conventional summarystatistics (i.e., daily or monthly means, maximums, minimums) fail tocapture this information. More accurate credit scoring allows financialinstitutions to issue products such as micro-loans at more competitiverates.

An advantage of the processes herein is the ability to summarize complextemporal patterns in financial behaviour, and to use such summarizing toimprove financial products on offer. For example, the particularapplication to short-term micro-loans is important. These settings havea payback period of <6 months (compared to multiple years of moretraditional loans). Therefore being able to identify temporal patternsin this short time scale is important to calculating more accuratecredit scores and limits.

Various embodiments of the invention are described more fullyhereinafter with reference to the accompanying drawings. The inventionherein may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth in the drawings;rather, these embodiments are provided to provide further illustrativenon-limiting examples. Arrowheads in the figures are provided merely asexamples of directions for the flow of data but are not exhaustive andare not meant to be limiting—i.e., data may flow (where appropriate) indirections that are not shown by arrowheads in the figures. Similarnumbers in different figures are meant to refer to similar components.

With reference to FIG. 1, there is shown a flow chart showing dataprocessing by a system according to an aspect of the invention. System200 is shown as receiving test panel data 150. The test panel data isobtained from data warehouse 100, which itself may be a standalonedatabase as shown or may be a component part of system 200 (althoughsuch embodiment is not shown). Test panel data 150 may optionally beidentified from a larger set of data. Within system 200 is trajectoryinference module 210, which identifies k trajectories from the paneldata. Furthermore within system 200 is decision classifier 220, whichassings trajectories to customers who were a part of the original paneldata obtained from data warehouse 100. In this way original panel dataare checked by the model that emerges from the analysis. That is,decision classifier 220 determines the types of data within the testpanel data, each type of data being subjected to GBTM analysis todetermine the k trajectories. System 200 also receives new user data110, which data may be stored in the data warehouse 100 or may be storedon a user device or elsewhere. The new user data is panel data and iscompared to the trajectories using trajectories comparing module 230. Inembodiments, new user data 110 is treated distinctly from test paneldata because such data are not subjected to decision classifier 220 butare rather directly compared with determined trajectories.

The system 200 identifies a trajectory of best fit and then determines aproduct and/or a variable based on the trajectory of best fit, whichidentification is used to create output 300.

With reference to FIG. 2, there is shown a flow chart showing data inputand output according to an aspect of the invention. System 200 receivesdata from three sources (which sources may be independent or which mayoverlap, in various embodiments): new user data 110, data warehouse 100,and telecom data 120. From these data, two types of output are prepared:user specific output 310 and predictive model output 320.

With reference to FIG. 3, there is provided a set of steps for carryingout an aspect of the methods disclosed herein. In the method, a providerserver or another suitable device receives (e.g., via a distributednetwork) anonymous financial data (400) pertaining to a financialvariable such as those described herein. The data is received frominternal sources (e.g., from a server associated with the same provideras the server carrying out the analysis) or from an external provider.The data is historical data and is sourced from a plurality of customers(users) over a period of time. The system stores such data (410) in adatabase, which storage may involve, where necessary, reconfigurationand/or reformatting (i.e., editing) of the data so as to be compatiblewith later algorithms and processes carried out by the system. Theediting of the data may further comprise automatically or manuallyselecting a subset of the original data received, particularly where thereceived data includes data that is not needed or not relevant to thelater algorithms. The system then applies GBTM to the stored (and, whereappropriate, edited) data (420). The output of the GBTM calculations isa number of trajectories, and the system characterizes (i.e.,classifies) the output trajectories (430).

With reference to FIG. 4, there is provided a set of steps for carryingout an aspect of the methods disclosed herein. In the method, userfinancial data is received (500) by a provider system or a third-partysystem contracted by the provider. The system optionally edits the userdata (e.g., to select a subset of the data, such as a specific timeperiod, or to exclude irrelevant data, or to exclude outlier data, etc.)and then compares the user data (510) to the trajectories determined asoutput from the GBTM algorithm on historical data. The system selects atrajectory that best fits the user data (520). The system then selects,based on the determined trajectory, a financial product to offer to theuser (530). The system then generates a message based on the selectedfinancial product on offer (540), and transmits the message (550) eitherto a user device or to a provider device.

With reference to FIG. 5, there is provided a set of steps for carryingout an aspect of the methods disclosed herein. In the method, arecipient device (i.e., either a user device or a provider device)receives a message (600) via a distributed network, such as via a LAN orWiFi or cellular network. The device parses the message (610) andextracts various components of the message (e.g., instructions forcarrying out an alteration to the device). In embodiments, the messagethen causes the device to alter a User Interface (620), such as bydisplaying an alert or the like. In embodiments, the messagealternatively (or in addition) causes the device to alter a setting ofthe device (630), such as by automatically creating a schedule of alertsto be transmitted to the user through a UI. In embodiments, the messagealternatively (or in addition) causes the device to alter an application(640) stored/operable on the device, such as by reformatting a bankingapplication to display various data pertaining to the financial product.

Throughout this disclosure, use of the term “server” is meant to includeany computer system containing a processor and memory, and capable ofcontaining or accessing computer instructions suitable for instructingthe processor to carry out any desired steps. The server may be atraditional server, a desktop computer, a laptop, or in some cases andwhere appropriate, a tablet or mobile phone. The server may also be avirtual server, wherein the processor and memory are cloud-based.

The methods and devices described herein include a memory coupled to theprocessor. Herein, the memory is a computer-readable non-transitorystorage medium or media, which may include one or moresemiconductor-based or other integrated circuits (ICs) (such, as forexample, field-programmable gate arrays (FPGAs) or application-specificICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs),optical discs, optical disc drives (ODDs), magneto-optical discs,magneto-optical drives, floppy diskettes, floppy disk drives (FDDs),magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITALcards or drives, any other suitable computer-readable non-transitorystorage media, or any suitable combination of two or more of these,where appropriate. A computer-readable non-transitory storage medium maybe volatile, non-volatile, or a combination of volatile andnon-volatile, where appropriate.

Throughout this disclosure, use of the term “or” is inclusive and notexclusive, unless otherwise indicated expressly or by context.Therefore, herein, “A or B” means “A, B, or both,” unless expresslyindicated otherwise or indicated otherwise by context. Moreover, “and”is both joint and several, unless otherwise indicated expressly or bycontext. Therefore, herein, “A and B” means “A and B, jointly orseverally,” unless expressly indicated otherwise or indicated otherwiseby context.

Embodiments of the present invention may be a system, a method, and/or acomputer program product at any possible technical detail level ofintegration. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

One or more embodiments can make use of software running on ageneral-purpose computer or workstation. With reference to FIG. 6, in acomputing node 650 there is a computer system/server 652, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 652 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 652 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 652 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 6, computer system/server 652 in computing node 650 isshown in the form of a general-purpose computing device. The componentsof computer system/server 652 may include, but are not limited to, oneor more processors or processing units 656, a system memory 668, and abus 658 that couples various system components including system memory668 to processor 656.

The bus 658 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

The computer system/server 652 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 652, and it includes both volatileand non-volatile media, removable and non-removable media.

The system memory 668 can include computer system readable media in theform of volatile memory, such as random access memory (RAM) 670 and/orcache memory 672. The computer system/server 652 may further includeother removable/non-removable, volatile/nonvolatile computer systemstorage media. By way of example only, storage system 674 can beprovided for reading from and writing to a non-removable, non-volatilemagnetic media (not shown and typically called a “hard drive”). Althoughnot shown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to thebus 658 by one or more data media interfaces. As depicted and describedherein, the memory 668 may include at least one program product having aset (e.g., at least one) of program modules that are configured to carryout the functions of embodiments of the invention. A program/utility680, having a set (at least one) of program modules 682, may be storedin memory 668 by way of example, and not limitation, as well as anoperating system, one or more application programs, other programmodules, and program data. Each of the operating system, one or moreapplication programs, other program modules, and program data or somecombination thereof, may include an implementation of a networkingenvironment. Program modules 682 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 652 may also communicate with one or moreexternal devices 654 such as a keyboard, a pointing device, a display664, etc., one or more devices that enable a user to interact withcomputer system/server 652, and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 652 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 662. Still yet, computer system/server 652can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 660. As depicted, network adapter 660communicates with the other components of computer system/server 652 viabus 658. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 652. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

It is understood in advance that although this disclosure includes adetailed description on cloud computing below, implementation of theteachings recited herein are not limited to a cloud computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any other type of computingenvironment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Computing node 650 in FIG. 6 can be an example of a cloud computingnode. Computing node 650 is only one example of a suitable cloudcomputing node and is not intended to suggest any limitation as to thescope of use or functionality of embodiments of the invention describedherein. Regardless, computing node 650 is capable of being implementedand/or performing any of the functionality set forth hereinabove. It isalso to be understood that computing node 650 is not necessarily a cloudcomputing node.

Referring now to FIG. 7, illustrative cloud computing environment 760 isdepicted. As shown, cloud computing environment 760 comprises one ormore cloud computing nodes 750 with which local computing devices usedby cloud consumers, such as, for example, a wearable device (notexplicitly shown), a personal digital assistant (PDA) or cellulartelephone 764A, desktop computer 764B, laptop computer 764C, and/orautomobile computer system 764N may communicate. Nodes 750 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 760 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 764A-Nshown in FIG. 7 are intended to be illustrative only and that computingnodes 710 and cloud computing environment 760 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby cloud computing environment 760 (FIG. 7) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 8 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 860 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 861;RISC (Reduced Instruction Set Computer) architecture based servers 862;servers 863; blade servers 864; storage devices 865; and networks andnetworking components 866. In some embodiments, software componentsinclude network application server software 867 and database software868.

Virtualization layer 870 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers871; virtual storage 872; virtual networks 873, including virtualprivate networks; virtual applications and operating systems 874; andvirtual clients 875.

In one example, management layer 880 may provide the functions describedbelow. Resource provisioning 881 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 882provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 883 provides access to the cloud computing environment forconsumers and system administrators. Service level management 884provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 885 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 890 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 891; software development and lifecycle management 892;virtual classroom education delivery 893; data analytics processing 894;transaction processing 895; and financial information processing 896,which may implement the functionality described above with respect toFIGS. 1-8.

It is to be understood that while the invention has been described inconjunction with examples of specific embodiments thereof, that theforegoing description and the examples that follow are intended toillustrate and not limit the scope of the invention. It will beunderstood by those skilled in the art that various changes may be madeand equivalents may be substituted without departing from the scope ofthe invention, and further that other aspects, advantages andmodifications will be apparent to those skilled in the art to which theinvention pertains. The pertinent parts of all publications mentionedherein are incorporated by reference. All combinations of theembodiments described herein are intended to be part of the invention,as if such combinations had been laboriously set forth in thisdisclosure.

Examples

In an example, records are made available for each individual user overa long period of time, such as one year. With such panel data,trajectories are created that depict the various trends such as savingsbehavior, frequency of transactions and ability to take loans.

With such trends in place, data from a new user is obtained andcharacterized into several major categories using a trajectory inferencethat classifies scattered records into x-number of major trends.

These trajectories may fit to certain archetypes. For example, largemonthly deposits suggest a salaried worker whereas infrequent use maysuggest a day-laborer. Seasonal activity may be associated with ruralagriculture. Large withdrawals in certain months may suggest payment forschool fees.

With such trends in place, for a new user with a defined set ofcharacteristics the trend in which the new user falls is predicted. Withsuch a trajectory score combined with other credit scoring attributes,an accurate credit score is provided for the new user.

An example analysis is P(Y)=Sum(π(x)P(Y)). The term “savings amount” isused in the example below, but other metrics over time would work aswell. The distribution (trajectory) of customer i's savings (Y_(i)) isdescribed over time (or age of customer i). This is P(Y_(i)|Age_(i)). Itis assumed that these distributions come from a (possible) mixture of Jdifferent distributions (i.e., trajectories). For example, if J=2, thentwo trajectories are available for comparison against the customer'stemporal data. The distribution P(Y_(i)|Age_(i)) is analysed through thelikelihood of J different possibilities. That is:

${P\left( {Y_{i}{Age}_{i}} \right)} = {\sum\limits_{j = 1}^{J}{\pi^{j}*{P\left( {{Y_{i}{Age}_{i}},{j;\beta^{j}}} \right)}}}$

Where:

j is indexing over the possible J trajectories;π^(j) is probability that the customer belongs to trajectory j;and the last term (ß) is the probability that distribution j actuallyresulted in the Y_(i) time series that was observed.

1. A method for processing financial information comprising: applying agroup-based trajectory model (GBTM) algorithm to time-dependentfinancial data for a first financial variable for a plurality of usersto determine a plurality of first financial variable trajectories; for aset of first financial variable time-dependent data for a new user,determining a best fitting trajectory from the plurality of firstfinancial variable trajectories; transmitting a message, via adistributed network, to a recipient device selected from a user deviceassociated with the new user and a provider device associated with aprovider, the message comprising content determined at least in part bythe best fitting trajectory, the message configured to alter a userinterface of the recipient device to display the content, wherein thecontent comprises an offer for a financial product, and wherein at leastone variable pertaining to the financial product is determined at leastin part by the best fitting trajectory; and coordinating reconfigurationof the recipient device based on the offer for a financial product. 2.The method of claim 1, wherein the reconfiguration is a non-volatilechange to the recipient device and is selected from: a change in asetting in the device; an altered user interface of the recipient deviceto display one or more variables about the offered financial product; analtered application stored in a memory of the recipient device; anautomatically configured alert schedule to provide scheduled remindersto the user or provider; and an updated profile of the user on theprovider device.
 3. The method of claim 1, wherein the first financialvariable is selected from a savings account balance, an outstandingcredit balance, a credit capacity, a length of credit history, a numberof deposits, a number of withdrawals a number of loans, a number ofrepayments, a number of balance queries an amount of interest earnedover time, an amount of largest deposit, an amount of largestwithdrawal, an average deposit, and an average withdrawal.
 4. The methodof claim 1, wherein the plurality of first financial variabletrajectories comprises between 2 and 10 trajectories.
 5. The method ofclaim 1, further comprising applying the GBTM algorithm totime-dependent financial data for a plurality of additional financialvariable for the plurality of users to determine a plurality offinancial variable trajectories for each of the plurality of additionalfinancial variables.
 6. The method of claim 1, further comprisingapplying the GBTM algorithm to time-dependent financial data for aplurality of additional financial variable for the plurality of users todetermine a plurality of financial variable trajectories for each of theplurality of additional financial variables, and further comprisingdetermining best fitting trajectories for each of the additionalfinancial variables for the new user from the plurality of financialvariable trajectories for each of the plurality of additional financialvariables.
 7. The method of claim 1, wherein the message is transmittedto the provider device and wherein the content further comprisescharacterization of the new user based on the best fitting trajectory.8. The method of claim 1, wherein the at least one variable is selectedfrom a loan amount, an interest rate, and a repayment schedule.
 9. Asystem comprising: a processor; a memory coupled to the processor, thememory configured to store program instructions for instructing theprocessor to carry out the method of claim
 1. 10. The system of claim 9,further comprising a trajectory module configured to apply the GBTMalgorithm to determine the plurality of first financial variabletrajectories.
 11. The system of claim 9, further comprising a trajectorycomparison module configured to compare the set of first financialvariable time-dependent data for the new user to determine the bestfitting trajectory from the plurality of first financial variabletrajectories.
 12. A method comprising: obtaining time-dependentfinancial data for a plurality of users between a first time and asecond time; applying an algorithm to reduce the time-dependentfinancial data for a plurality of users to a plurality of representativetrajectories; obtaining time-dependent financial data for a new userbetween the first time and the second time; comparing time-dependentfinancial data for the new user with the trajectories, and identifyingthe trajectory of best fit; determining a financial product for the newuser at least in part based on the trajectory of best fit; automaticallyexecuting an electronic fund transfer into a user account associatedwith the new user based on the determined financial product; andaltering a user interface to display the automatic transfer of funds.13. The method of claim 12, wherein the financial product is a loan, andwhere the loan amount, interest rate, and repayment schedule aredetermined based on the trajectory of best fit.
 14. The method of claim12, wherein the plurality of users is greater than 1000 users, andwherein obtaining time-dependent financial data for the plurality ofusers between a first time and a second time comprises obtaining dailydata for a period of at least one week.
 15. The method of claim 12,wherein the number of trajectories is at least 10 times fewer than thenumber of users.
 16. The method of claim 12, wherein the user account isa mobile money account associated with a mobile account.
 17. The methodof claim 12, wherein the financial product is further determined basedon telecom data pertaining to the new user.
 18. The method of claim 12,wherein the time-dependent financial data for a plurality of userscomprises data for a plurality of financial variables, and wherein thealgorithm is applied separately to each of the plurality of financialvariables.
 19. The method of claim 12, wherein the user interface is ona user device associated with the new user, and wherein the methodfurther comprises transmitting a message to the user device, the messageconfigured to alter the user interface to indicate the determinedfinancial product and an electronic funds transfer status.
 20. A systemcomprising: a processor; a memory coupled to the processor, the memoryconfigured to store program instructions for instructing the processorto carry out the method of claim 12.